Suppose the following numpy array: I want to know the fastest way to generate the following operation: (1) How to efficiently create matrix “result” (because n >> 0 can be very large) ? (2) Does this matrix have a particular name ? Answer This is a bit faster: cursory benchmarks, nothing scientific. (timeit 100 times with arr):
Tag: numpy
how to split a numpy array into subarrays based on values of one colums
I have a big numpy array and want to split it. I have read this solution but it could not help me. The target column can have several values but I know based on which one I want to split it. In my simplified example the target column is the third one and I want to split it based on
Check for max value of iterative sum in pandas
I have a pandas dataframe with some data: I’d like to sum them, one at a time, and at every summation check the totalt sum, an then extract the highest value achieved of the total sum. So the first sum would be element 0, with a value of -0.000601, the send would be -0.000601 + 0.000001=-0.0006, and this would be
Replacing chunks of numpy array on condition
Let’s say I have the following numpy array, of 1’s and 0’s exclusively: I want to group all elements into chunks of 3, and replace the chunks by a single value, based on a condition. Let’s say I want [0,1,1] to become 5, and [0,1,0] to become 10. Thus the desired output would be: All possible combinations of 1’s and
Unable to convert tensorflow.python.framework.ops.Tensor object to numpy array for passoing it in sklearn.metrics.cohen_kappa_score function
I thought of implementing kappaScore metrics using sklearn.metrics.cohen_kappa_score Error I get when I try to run this code: Here the type of y_true and y_pred requires to be in list or numpy array But the type of y_true and y_pred are, When directly try to print it (i.e, without type() function), it shows like this: Unable to use y_true.numpy() (Convert
How to multiply specific rows/columns of matrices with each other in python?
I have to input matrices of shape I want to multiply each row (each n of size 3) with its correspondence of the other matrix, such that i get a (3,3) matrix for each row. When im trying to just use e.g. m1[0]@m2.T[0] the operation doesnt work, as m[0] delivers a (3,) list instead of a (3,1) matrix, on which
Connecting to random points in a 2d numpy array based on distance
I have a 2d numpy array and select a random coordinate position (say 10×10 array and start at position 2,3). I want to randomly connect to 40% of the other points in the 2d array effectively generating a list of tuples [(x1, y1), (x2, y2) …] where the list is 40% of the other coordinates. An additional constraint, however, is
In Python, I want to add a number to each element in a list, but I get [[0,1],2] instead of [0,1,2]. How to fix this?
I have For each element in listB, I want to extend it to a length-3 element by adding a number that is in listall but not in this element. My desired output is the following list: As a first step, I tried the following code: However, the output I got is: This is far from what I wanted. As each
Numpy 2D-array sets the value of a cell when it is not needed
I’m trying to write minesweeper in python. A bomb is a cell with a value less than 0. The values of cells that differ from the position of the bomb cell by one column and one row in all directions should increase by 1. But the values of cells that are far from the bomb often increase. Please help me
How to use tf.repeat() to replicate a specific column/row/slice?
this thread explains well the use of tf.repeat() as a tensorflow alternative to np.repeat(). one functionality which I was unable to figure out, in np.repeat(), a specific column/row/slice can be replicated by supplying the index. e.g. is there any tensorflow alternative to this functionality of np.repeat()? Answer You could use the repeats parameter of tf.repeat: where you get the first